sign language representation for machine translation

Sign Language Representation for

Machine Translation

Sara MorrisseyNCLT/CNGL Seminar Series

1st April, 2009

Why is there no writing system?

• Social reasons• Variation and demographic spread

• Political reasons• Recognition

• Linguistic reasons• Visual-gestural-spatial languages, simultaneous

phoneme production

Implications of the lack of writing system

• …for Deaf people• Forced use language not native

• …for the languages• social acceptance standardisation (Pizzuto, 2006)

• … for MT• Limits availability of domain-specific corpora• No standards, difficult to compare systems• Significance of results on small datasets• Difficult to use NLP tools developed for spoken langs

Sign Language Representation Formats

• Linear• Stokoe Notation, HamNoSys

• Multi-level• Gloss, Partition/Constitute, Movement-

Hold, SiGML

• Iconic• SignWriting

Linear Symbolic Notations

Stokoe Notation: “don’t know”

HamNoSys Notation: “nineteen”

Multi-level Representations

<?xml version="1.0"encoding="iso-8859-1"?><!DOCTYPE sigml SYSTEM "http://..."><sigml><hamgestureal sign gloss="going to DGS"><sign manual both hands="true"><handconfig handshape="finger2“ thumbpos="out"/><handconfig extfidir="uo“ palmor="1"/>

Movement-Hold

Partition/Constitute

Gloss Annotation SiGML

Iconic

Sign Writing

But different groups, different requirements

(Pizzuto et al, 2006): the aspect of a language chosen for its

representation, is largely dictated by the society and culture developing the writing system and what purpose and settings such communication is required for.

Deaf, linguists, language processors…

Requirements for MT

• large bilingual domain-specific corpus of good quality digital data

• gold standard reference• segmentation algorithms for separating

words, phrases and sentences • alignment methodologies for these units. • searching the source and target texts• acceptable capturing of the language for

output

Discussion of current methods

• Stokoe (Stokoe, 1960)– Difficult to capture classifiers and NMFs– Decontextualised signs only– ASCII version (Mandel, 1993)

• HamNoSys (Prillwitz, 1989)– NMFs included– Subsection of 150 symbols for handwriting purposes– Mac usage, Windows font

Discussion of current methods (2)

• Gloss Annotation: (Leeson et al., 2006, Neidle et al., 2002)

– Most commonly used in MT and by linguists– No universal conventions– Extensible– Using one language to describe another– Allows for simultaneous timed logging of features– Tools widely available– SL and linguistic knowledge a requirement– No knowledge of supplementary symbolic system

required

Discussion of current methods (3)

• Partition/Constitute (Huenerfauth, 2005)– Captures movement, classifier and spatial info– Comprehensive, hierarchical rep’n– Implicit use of gloss terms

• Movement-Hold (Liddell & Johnson, 1989)– Numerically-encoded handshapes– Multi-layer– Used with recognition technology (Vogler & Metaxas,

2004)

Discussion of current methods (4)

• SiGML (Elliott et al., 2004)– Describes HamNoSys for animation (ViSiCAST)– Double representation

• SignWriting (Sutton, 1995)– Compact icons– Information displayed in one place– Advocated by SL linguists and growing Deaf– Not currently machine readable

Worked Example

• “Data-driven Machine Translation for Sign Languages” (Morrissey, 2008)

• MaTrEx MT system• Glossed Annotations of Irish Sign Language

(ISL) and German Sign Language (DGS)• Air Traffic Information System corpus of ~600

sentences• Translated and signed by native Deaf signers

Hand-crafted gloss annotation corpus

Translation Directions

MaTrEx Experiments

• ISL gloss-to-English text– Baseline– SMT– EBMT 1 – EBMT 2– Distortion limit

ISL-EN MaTrEx Experiments

BLEU WER PERAnnotation Baseline 25.20 60.31 50.42

SMT 51.63 39.32 29.79

EBMT 1 50.69 37.75 30.76

EBMT 2 49.76 39.92 32.44

EN-ISL MaTrEx Experiments

BLEU WER PERISL-EN

best scores 52.18 38.48 39.67

SMT 38.85 46.02 34.33

EBMT 1 39.11 45.90 34.20

EBMT 2 39.05 46.02 34.21

Other experiments• ISLDE, DGSDE, DGSEN

– ISL EN best scores, by 6.38% BLEU– EBMT 1 chunks improves for ISL-DE only– EBMT 2 chunks improves for ISL-DE only

• DEISL, DEDGS, ENDGS– ENDGS best scores, by 1.3% BLEU– EBMT 1 chunks improves for ENDGS & ENISL– EBMT 2 chunks improves for all

• Comparison with RWTH system– We’re better! ~2-6% BLEU

• ISL video recognition• Speech output

ISL Animation• Poser software• Hand-crafted 66

videos, 50 sentences• Played in sequence• 4 Deaf evaluators• 2 x 4-point scale• 82% - intelligibility• 72% - fidelity• Questionnaire

Demo

Thesis Conclusions• Good results can be obtained• Glossing most appropriate, but not going forward

– Allowed linguistic-based alignment– Linear, easily accessible format– Lack of NMF detail, time-consuming, not considered

adequate representation of language• EBMT chunks show potential but require more

development• Development of animation module

Where do we go from here?(the words are coming out all weird…)

• What is the most appropriate SL representation for MT?– Adequately represents the language,– Animation production, – Facilitates the translation process.

Rep’n overview, redux• Glossing: machine readable, doesn’t adequately

represent the language or facilitate animation• Stokoe: ASCII version, not adequate rep’n• Partition/Constitute: multi-layered, uses glosses• Movement-Hold: multi-layered, uses glosses• Sign Writing: compact icons, accepted, potential

readability, not machine readable at present• …• HamNoSys & SiGML: machine readable,

comprehensive description, adapted for animation, suited to SMT

The Future…

• Explore HamNoSys in practice• MT in medical domain, Health Ireland

Partner GP work group questionnaire• Human Factors• Minority Language MT

Thank you for listening

Yep, it’s the end!

I hope it wasn’t too long

Any questions?